This paper describes Orchid, a system that converts declarative mapping specifications into data flow specifications s) and vice versa. Orchid provides an abstract operator model t...
With the increasing popularity of largescale probabilistic graphical models, even "lightweight" approximate inference methods are becoming infeasible. Fortunately, often...
We consider learning models for object recognition from examples. Our method is motivated by systems that use the Hausdorff distance as a shape comparison measure. Typically an ob...
The problem of consistently engineering large, complex software systems of today is often addressed by introducing new, "improved" models. Examples of such models are arc...
Our goal is to fit the multiple instances (or structures) of a generic model existing in data. Here we propose a novel model selection scheme to estimate the number of genuine str...